Real-Time Traffic Prediction Using Deep Spatiotemporal Learning

Abstract

Abstract This study explores the use of deep spatiotemporal learning for real-time traffic prediction. Traditional traffic forecasting methods often rely on historical averages and fail to capture complex spatial and temporal dependencies. This research applies Graph Convolutional Networks (GCN) to model spatial relationships between roads and Long Short-Term Memory (LSTM) networks to capture temporal traffic patterns. Traffic data from sensors, GPS devices, and monitoring cameras were collected, cleaned, and processed for modeling. Insights from this study highlight the potential for real-time traffic management, route optimization, and smart city planning. Keywords: Traffic prediction, deep learning, spatiotemporal modeling, LSTM, GCN, real-time forecasting.